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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 16 Issue 3, 2025.
Abstract: The maximum degree of function node of pattern matrix (PM) dominates the detection complexity of belief propagation algorithm for pattern division multiple access (PDMA) systems. This work proposes a method to search the optimal PM ensemble for PDMA system under constrained detection complexity. This issue is converted to find the optimal variable node (VN) degree distribution (DD) of PM with function node DD concentrated. Utilizing extrinsic information transfer chart (EXIT) techniques, the DD of PM with overload rate of 150%is obtained and its DD is designed by progressive edge growth (PEG) algorithm. The performance of this PDMA system is evaluated and compared with the ones of the same overload rate in literature to verify the effectiveness of the proposed method. Furthermore, for iterative detection and decoding (IDD), the concatenated LDPC code is optimized to enhance the overall performance. EXIT analysis and Monte Carlo simulations confirm that the designed pattern matrix outperforms other pattern matrix about 2.3 dB in bit error rate when both schemes employ the same LDPC code, and 0.2 dB when using the optimized codes respectively.
Hanqing Ding, Jiaxue Li and Jin Xu, “The Optimization Design of the Pattern Matrix Based on EXIT Chart for PDMA Systems” International Journal of Advanced Computer Science and Applications(IJACSA), 16(3), 2025. http://dx.doi.org/10.14569/IJACSA.2025.01603109
@article{Ding2025,
title = {The Optimization Design of the Pattern Matrix Based on EXIT Chart for PDMA Systems},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01603109},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01603109},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {3},
author = {Hanqing Ding and Jiaxue Li and Jin Xu}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.